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The #1 Qlik Productivity Killer: Unplanned Work 

Qlik Productivity

For many BI developers, the real challenge isn’t building load processes and great visualizations; it’s the unplanned work that sneaks into the day. A failed reload that needs urgent fixing, a dependency that changes without warning, or a sheet tweak that has to be redone: these minor interruptions add up, pulling focus away from real innovation. The result isn’t failure, but inefficiency—time spent on tasks that weren’t planned but couldn’t be ignored. Data-driven development helps prevent this by giving teams context and visibility into usage, risks, and potential issues throughout the development lifecycle in the UX of the BI tool. With that knowledge, BI teams can increase quality, avoid double work, and reduce unexpected errors. Let me explain how this works in Qlik first, but also highlight its benefits in Power BI, which is more focused on self-service analytics.

Unplanned Work Is the Biggest Time Drain in Business Intelligence, and It’s Largely Preventable

Across DevOps, unplanned work is the biggest disruptor of productivity. It displaces planned work, causes delays, and creates a vicious cycle of firefighting that prevents improvement.  According to an article in ComputerWorld by Gene Kim, author of The Phoenix Product, in high-performing analytics teams, unplanned work is kept below 5% of total effort. But in reactive environments, more than 50% of BI developer time can be lost to surprise issues, broken dashboards, and fire drills. Further, according to a Forrester impact study, organizations that reduce unplanned downtime by 60–70% often reclaim thousands of hours annually, in some cases, 3,000 hours!

Metadata Gives Qlik Teams Foresight to Avoid Mistakes

For most Qlik engineers, it is hard to get insight into how their apps or sheets are being used, by whom, who last changed it, and how their changes will affect. As a result, they operate on assumptions, gut feelings, and outdated information. When metadata is made visible during the development process AND in Qlik, teams can spot potential issues and address them. For example, if a developer sees that another teammate is actively working on the same app, they can avoid taking a copy from production and override each other’s work. Instead, collaborate in a development environment and merge their results. It’s a situation known all too well at organizations. It could also be that another developer is working in a different area, for example, another sheet, and this change is unforeseen and deployed to production. The result? Besides, hours of valuable work might be lost, and it also creates unplanned work that could have been avoided with metadata transparency.

Usage Data Helps Qlik Developers Focus on What Matters Most

Besides information on who is actually working in the app and who has made what changes in the past, insight into current usage data is very helpful. Qlik developers are flooded with requests, but not every report or dashboard delivers equal business value. With metadata-driven insights, teams can see which assets are most heavily used, which ones support key decisions, and where to focus their effort. This leads to more thoughtful prioritization and faster delivery with less backtracking.

Imagine knowing that Report A is used daily by executives, while Report B hasn’t been touched in months. Now you know where your time will have the most significant impact, and you avoid wasting time on low-value work.

Continuous Insight Keeps Business Intelligence Teams in Sync and Productive

“Analytics over analytics”, visibility into usage, performance, and change is what keeps business intelligence teams agile. When BI developers see how their apps are performing and how users are interacting with them, they stay focused on results. It creates shared context across teams, reducing siloed work and unnecessary overlap.

One company recently had to postpone a meeting addressing additional business requirements due to a BI system outage. The release of a new report had caused unexpected issues. If metadata and quality signals had been embedded in the development process, the team could have caught the problem earlier and stayed on track with both deliverables and strategy.

The Right Solution for Turning Metadata into Actionable Intelligence for BI Developers

DevJeannie is the tool that brings data-driven development to life by making helpful metadata visible and accessible, right inside the Qlik UX. In addition to the examples mentioned earlier, where metadata helps the Qlik developer, DevJeannie integrates with QSDA Pro to display performance issues and quality checks within your workflow. You’ll see alerts like:

“This app has 6 issues:
• 2 broken expressions
• 3 unused variables
• 1 inefficient chart
Click to view owner, usage stats, and recommended next steps.”

This proactive feedback loop helps BI developers prevent issues before they become support tickets.

Additionally, DevJeannie will warn you when there are potential performance issues when the app is deployed to Prod and will face large data sets.

While DevJeannie surfaces critical metadata inside the Qlik platform, the value of metadata-driven development extends far beyond Qlik alone. Power BI teams face many of the same challenges. The principles behind DevJeannie apply across BI tools: when developers have access to metadata in their workflow, they make better decisions and avoid unplanned work.

DevJeannie Gives Power BI Developers Context to Avoid Disruption Too

Typically, Power BI is implemented differently from Qlik. The platform is designed for self-service, and most analysts start working from the desktop publishing into the Power BI service.

Since more people create data models resulting in data models, the risks of inconsistencies between semantic models are a real concern. For example, while creating reports in Power BI, different developers could create their own DAX measures for “Net Product Sales”, some would exclude the cost of goods while others would include mandatory services. Without a clear visibility into how definitions are used, inconsistent metrics make their way into executive reports, eroding trust and triggering tickets, and causing unplanned work.

A comprehensive metadata catalog that includes all objects of both BI engineers and citizen developers, highlights duplicate DAX expressions, where they were used, and how they differed from standard definitions. Content creators are able to align and avoid the scramble. As a result, unplanned work is reduced.

Like Qlik, when Power BI teams have access to usage and dependency metadata, they make safer changes, avoid unplanned work, and keep delivery on track.

Data-Driven Development Helps You Deliver Faster with Fewer Disruptions

Whether working with Qlik or Power BI, DevJeannie enables teams to:

  • Build with confidence by seeing how changes impact others before deploying
  • Focus on high-value work by using usage insights to prioritize requests
  • Avoid preventable mistakes by surfacing risks and conflicts earlier in the process

The key to better business intelligence isn’t just building dashboards; it’s removing the friction that slows your team down. With DevJeannie, BI teams reduce the chaos of unplanned work and get back to what matters: delivering business value.

Want to know more about how metadata helps your team save time? Contact us.

People Also Ask (FAQs)

Q1. What is the #1 productivity killer in Qlik and Power BI development?
The biggest productivity killer is unplanned work—unexpected fixes, rework, and interruptions caused by broken dashboards, duplicate efforts, or hidden dependencies. It pulls BI developers away from planned tasks and delays delivery.

Q2. How does unplanned work impact Business Intelligence teams?
Unplanned work can consume up to 50% of a developer’s time in reactive environments. This leads to constant firefighting, slower innovation, and frustrated business users. High-performing teams reduce unplanned work to less than 5% of their workload.

Q3. How can metadata reduce unplanned work in Qlik and Power BI?
Metadata gives business intelligence teams visibility into usage, dependencies, and performance. This foresight prevents mistakes, reduces duplication, and helps prioritize the most valuable dashboards and reports.

Q4. What is DevJeannie, and why is it important for BI developers?
DevJeannie is a metadata-driven development tool that integrates into Qlik and Power BI. It surfaces issues like broken expressions, unused variables, and duplicate DAX measures, allowing BI developers to catch problems early before they become costly rework.

Q5. Can Power BI developers benefit from metadata transparency like Qlik teams?
Yes. Power BI developers face risks such as conflicting DAX definitions and duplicate metrics. Metadata transparency helps align definitions, reduce inconsistencies, and keep delivery on track.

Q6. What are the measurable benefits of reducing unplanned work in Business Intelligence?
According to industry studies, organizations that cut unplanned downtime by 60–70% reclaim thousands of hours annually—in some cases more than 3,000 hours per year. This translates to higher efficiency, faster delivery, and greater business value.

Quick Takeaway:
Reducing unplanned work with metadata-driven development helps Qlik and Power BI teams build with confidence, focus on high-value analytics, and deliver results faster—with fewer disruptions. Tools like DevJeannie make this possible by embedding metadata directly into the BI workflow.

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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
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Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

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Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
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When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.